"""simple docstring"""



from __future__ import annotations

SCREAMING_SNAKE_CASE__:str									=			10



def 	_lowerCamelCase(	a				):
       __a       =    1
       __a       =    max(a				)
       while placement <= max_digit:
              # declare and initialize empty buckets
              __a       =    [[] for _ in range(a				)]
              # split list_of_ints between the buckets
              for i in list_of_ints:
                     __a       =    int((i / placement) % RADIX				)
                     buckets[tmp].append(a				)
              # put each buckets' contents into list_of_ints
              __a       =    0
              for b in range(a				):
                     for i in buckets[b]:
                            __a       =    i
                            a += 1
        # move to next
              placement *= RADIX
       return list_of_ints


if __name__ == "__main__":
   import doctest

   doctest.testmod()

"""simple docstring"""



from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
SCREAMING_SNAKE_CASE__:str									=			HfArgumentParser(InitializationArguments)
SCREAMING_SNAKE_CASE__:List[str]									=			parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
SCREAMING_SNAKE_CASE__:List[Any]									=			AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
SCREAMING_SNAKE_CASE__:Union[str, Any]									=			{
    """vocab_size""": len(tokenizer),
    """scale_attn_by_inverse_layer_idx""": True,
    """reorder_and_upcast_attn""": True,
}

# Load model config (GPT-2 large in this case)
SCREAMING_SNAKE_CASE__:Optional[int]									=			AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
SCREAMING_SNAKE_CASE__:Tuple									=			AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)
